1) Commonly used notation in deep learning

2) Logistic Regression - Basic Intuition

3) Logistic Regression - Loss and Cost Function 

4) Gradient Descent Algorithm - Basic Intuition

Hyperparameters and tuning strategies for Random Forest

Why Random Forest is my favourite Machine Learning model?

Decision Tree entropy information, gain, gain ratio lecture

Jupyter Notebooks - 

1) https://towardsdatascience.com/random-forest-in-python-24d0893d51c0

Filling the missing values with Random Forests 

Seismological application of above - https://pubs.geoscienceworld.org/ssa/srl/article/doi/10.1785/0220220103/616607/Using-Machine-Learning-Techniques-with-Incomplete

Random Forest for Imbalanced Dataset

Python code - https://imbalanced-learn.org/stable/references/generated/imblearn.ensemble.BalancedRandomForestClassifier.html

Paper Link - https://www.jmlr.org/papers/volume18/16-365/16-365.pdf

Random Forest outperform other techniques - 

Classic reference - https://www.jmlr.org/papers/volume15/delgado14a/delgado14a.pdf?source=post_page---------------------------

Random Forest Regression - 

https://towardsdatascience.com/random-forest-regression-5f605132d19d

Strategies and Tactics for Regression on Imbalanced Data

ROC and AUC : Intuitive Explanation

ROC and AUC -Clearly explained

ROC_and_AUC_towards_data_science

1) https://www.aptech.com/blog/permutation-entropy/#:~:text=Permutation%20Entropy%20(PE)%20is%20a,Henry%20and%20Judge%2C%202019).

1) Very simplified explanation of Recurrent Neural Networks and problem of vanishing gradients

2) Very intuitive explanation about LSTMs

3) General Starting notes about RNN

4) Nice blog about LSTMs

5) Andrej Carpathy's (Former Head of AI at Tesla) Blog

1) Math behind PCA (V Imp) - https://towardsdatascience.com/the-mathematics-behind-principal-component-analysis-fff2d7f4b643

2) Interesting blog on PCA - https://builtin.com/data-science/step-step-explanation-principal-component-analysis

1) Tsfel - https://tsfel.readthedocs.io/en/latest/descriptions/get_started.html

github - https://github.com/fraunhoferportugal/tsfel

2) Pyopy - https://github.com/strawlab/pyopy

3) Featuretools - https://featuretools.alteryx.com/en/stable/

4) A comprehensive list of time series feature extractors - https://www.sktime.org/en/stable/related_software.html

1) https://mljar.com/blog/feature-importance-in-random-forest/

2) SHAP feature importance - https://towardsdatascience.com/a-novel-approach-to-feature-importance-shapley-additive-explanations-d18af30fc21b

3) https://christophm.github.io/interpretable-ml-book/shap.html

1) Good review of autoencoders - https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798 - 6 July, 2023

Recursive Feature Elimination - https://towardsdatascience.com/powerful-feature-selection-with-recursive-feature-elimination-rfe-of-sklearn-23efb2cdb54e

1) Basic of an ANN and examples using LR, SR and ANN - https://towardsdatascience.com/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6 - 8 July, 2023. 

2) Basics of a CNN and visualization of input feature maps, filters and output layers - https://medium.com/towards-data-science/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2 - 11 July, 2023.